Expert Systems: Design and DevelopmentIntroduction to expert systems; major characteristics of expert systems; knowledge representation; inference techniques; MYCIN; rule-based expert systems; backward-chaining rule-based systems; designing backward-chaining rule-based systems; forward-chaining rule-based systems; designing forward-chaining rule-based system; bayesian approach to inexact reasoning; certainty theory; fuzzy logic; frame-based expert systems; designing a frame-based expert systems; induction systems; knowledge acquisition; knowledge engineering; appendix; index. |
Contents
Major Characteristics of Expert Systems | 26 |
Knowledge Representation | 52 |
Inference Techniques | 90 |
Copyright | |
19 other sections not shown
Common terms and phrases
approach Artificial Intelligence backward-chaining certainty factor CF value chaining Chapter class frame Client's concepts database decision tree defined depth-first search determine diagnosis discussed display drug end-user evaluation expert system development expert system project fault forward-chaining frame hierarchies frame-based systems function fuzzy logic fuzzy set given goal heuristic human expert hypothesis illustrate important induction inexact reasoning inference engine inheritance interface interview issues KBS applications KBSS knowledge acquisition knowledge base knowledge elicitation knowledge engineer knowledge representation knowledge-based systems memory meningitis method MYCIN node objects obtain operation organization patient performance premise problem problem-solving procedures Prolog question recommendation represent requires rule-based systems rules semantic network session shell tools shown in Figure slot solution solving specific step strategy structure system was developed system's knowledge technique temperature thermostat variable XCON
References to this book
Artificial Intelligence: A Guide to Intelligent Systems Michael Negnevitsky No preview available - 2005 |